Learning Solutions for Healthcare Fraud Detection

Learning Solutions for Healthcare Fraud Detection

By Capital Markets CIO Outlook | Monday, December 17, 2018

The viability of most healthcare systems revolves around competent and capable medical providers and a solid infrastructure. Both aspects can be immutably damaged by fraud and waste. Medical Frauds are those who cheat the system to collect healthcare data. The major fraud cases are from healthcare providers because there are so many providers in current healthcare solutions that are not updated to identify frauds in the vast amount of information.

By the year 2015, there were 67,000 pharmacies in the U.S. Prescription fraud is an example where a pharmacy automatically refills the prescription before patients request for a refill. Even if the patients were not using the prescriptions, the pharmacists could charge them for the same. One example of this problem was a single pharmacy paying more than $34000 to settle claims made by the Massachusetts Medicaid program. The lack of analytics solutions prevents organizations and insurers from identifying fraud. The same auto-refill case was found at SCAN Health Plan, Long Beach, CA. The companies started receiving tips from the customers stating that they were receiving lidocaine refills though they hadn’t ordered with some lidocaine cream for every two weeks. SCAN used Alteryx analytics platform to perform an in-depth analysis of data that removed pharmacies that practiced fraudulent billing from SCAN’s network. Analytics can find the fraud before an analyst searches for the exact problem. Machine Learning can be used to analyze refill patterns for individuals, pharmacies, and regions. These insights may be driven by changes in benefits, membership, or claiming behavior in response to integrity controls. ML should be included in the infrastructure where exceptions can be highlighted for the rectifications.

ML can detect the faults in data that indicate fraud and enable the analysts to perform in-depth analysis of the data which in turn reduces financial loss for providers. Users can be notified early in the fraud attempt that prevents the damage after identifying the issues physically. The analysis of the large volume of existing data can prevent frauds and start the recovery process, but machine learning predictive analysis can reduce the loss earlier and provide huge savings for healthcare.

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